Adaptive Neural-Fuzzy Sliding-Mode Fault-Tolerant Control for Uncertain Nonlinear Systems

Shiping Wen; Michael Z. Q. Chen; Zhigang Zeng; Tingwen Huang; Chaojie Li

doi:10.1109/TSMC.2017.2648826

Adaptive Neural-Fuzzy Sliding-Mode Fault-Tolerant Control for Uncertain Nonlinear Systems

Shiping Wen
, Michael Z. Q. Chen
, Zhigang Zeng
, Tingwen Huang
, Chaojie Li

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

92 Citations (Scopus)

Abstract

This paper proposes an adaptive neural-fuzzy sliding-mode control method for uncertain nonlinear systems with actuator effectiveness faults and input saturation. The parameter dependence of the control scheme is removed from the bound of actuator faults by updating online. A neural-fuzzy model is developed to approximate the uncertain nonlinear terms and a sliding-mode online-updating controller is developed to estimate the bound of the actuator with no prior knowledge of the fault. The asymptotic stability is verified via the Lyapunov method in the presence of actuator faults and saturation. Furthermore, the adaptive neural-fuzzy control method is extended to the uncertain faulty nonlinear systems with integral sliding-mode manifold as well as other popular sliding-mode surfaces. A numerical example is presented to demonstrate the effectiveness of the derived results. © 2013 IEEE.

Original language	English
Article number	7862291
Pages (from-to)	2268-2278
Journal	IEEE Transactions on Systems, Man, and Cybernetics: Systems
Volume	47
Issue number	8
DOIs	https://doi.org/10.1109/TSMC.2017.2648826
Publication status	Published - 1 Aug 2017
Externally published	Yes

Bibliographical note

Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

Funding

This work was supported in part by the Natural Science Foundation of China under Grant 61403152, Grant 61402218, Grant 61673187, and Grant 61673188, in part by the Research Grants Council of Hong Kong through General Research Fund under Grant 106140120, and in part by the NPRP Grant NPRP 8-274-2-107 from the Qatar National Research Fund (a member of Qatar Foundation).

Research Keywords

Actuator fault
actuator saturation
neural-fuzzy approximator
sliding-mode control (SMC)
uncertain nonlinear system

RGC Funding Information

RGC-funded

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10.1109/TSMC.2017.2648826

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title = "Adaptive Neural-Fuzzy Sliding-Mode Fault-Tolerant Control for Uncertain Nonlinear Systems",

abstract = "This paper proposes an adaptive neural-fuzzy sliding-mode control method for uncertain nonlinear systems with actuator effectiveness faults and input saturation. The parameter dependence of the control scheme is removed from the bound of actuator faults by updating online. A neural-fuzzy model is developed to approximate the uncertain nonlinear terms and a sliding-mode online-updating controller is developed to estimate the bound of the actuator with no prior knowledge of the fault. The asymptotic stability is verified via the Lyapunov method in the presence of actuator faults and saturation. Furthermore, the adaptive neural-fuzzy control method is extended to the uncertain faulty nonlinear systems with integral sliding-mode manifold as well as other popular sliding-mode surfaces. A numerical example is presented to demonstrate the effectiveness of the derived results. {\textcopyright} 2013 IEEE.",

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Adaptive Neural-Fuzzy Sliding-Mode Fault-Tolerant Control for Uncertain Nonlinear Systems. / Wen, Shiping; Chen, Michael Z. Q.; Zeng, Zhigang et al.
In: IEEE Transactions on Systems, Man, and Cybernetics: Systems, Vol. 47, No. 8, 7862291, 01.08.2017, p. 2268-2278.

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

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AU - Zeng, Zhigang

AU - Huang, Tingwen

AU - Li, Chaojie

N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

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AB - This paper proposes an adaptive neural-fuzzy sliding-mode control method for uncertain nonlinear systems with actuator effectiveness faults and input saturation. The parameter dependence of the control scheme is removed from the bound of actuator faults by updating online. A neural-fuzzy model is developed to approximate the uncertain nonlinear terms and a sliding-mode online-updating controller is developed to estimate the bound of the actuator with no prior knowledge of the fault. The asymptotic stability is verified via the Lyapunov method in the presence of actuator faults and saturation. Furthermore, the adaptive neural-fuzzy control method is extended to the uncertain faulty nonlinear systems with integral sliding-mode manifold as well as other popular sliding-mode surfaces. A numerical example is presented to demonstrate the effectiveness of the derived results. © 2013 IEEE.

KW - Actuator fault

KW - actuator saturation

KW - neural-fuzzy approximator

KW - sliding-mode control (SMC)

KW - uncertain nonlinear system

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EP - 2278

JO - IEEE Transactions on Systems, Man, and Cybernetics: Systems

JF - IEEE Transactions on Systems, Man, and Cybernetics: Systems

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M1 - 7862291

ER -

Adaptive Neural-Fuzzy Sliding-Mode Fault-Tolerant Control for Uncertain Nonlinear Systems

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Research Keywords

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